Paper Title : ONTOLOGY BASED DISEASE PREDICTION USING D-MATRIX
ISSN : 2394-2231
Year of Publication : 2021
MLA Style: S.Sambasivam MCA., M.Phil., M Mahendran "ONTOLOGY BASED DISEASE PREDICTION USING D-MATRIX " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: S.Sambasivam MCA., M.Phil., M Mahendran "ONTOLOGY BASED DISEASE PREDICTION USING D-MATRIX " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
Medical prognosis is a complicated task based on both scientific knowledge derived from state of the art and medical judgment inferred from clinician experience. In recent years, many home health monitoring systems have been proposed. Patients affected by a specific disease are monitored by means of a medical protocol, based on periodical measurements through biomedical transducers of some physiological parameters related to the disease. Among them, one of most promising trends is related to noninvasive monitoring, owing to the intrinsic lack of the physical presence of a caregiver. The project proposes procedure to detect critical conditions of a patient, affected by a specific disease, at an early stage in absence of clinician. The procedure is to be integrated inside a remote health care system for patients at home, where some physiological parameters related to a specific disease are being monitored. A significant variation in the monitored parameters can lead the patient to a critical state, thus the proposed method is aimed at predicting a possible future bad condition of the patient on the basis of past measurements. The main scope of the project is used to predict the disease with specified symptoms by the patient and based on the previous case history of the patient. In this project, the set the details such as patient name, symptoms, symptoms with multiple meaning. The D-Matrix is used to used to find disease based result.Typically, the acquired data are sent via Internet to a remote center, where clinicians analyze the data and take decisions. The experimental evaluation is designed using Net Beans-6.8. The coding language used is Java. The back end used is MS SQL Server 2000.
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—Medical Prognosis, D-matris, Non invasive, Machine Learning, Patients.